{"id":"W4379511426","doi":"10.21428/594757db.6ee355e7","title":"Exploring Preferential Label Smoothing for Neural Network-based Classifiers","year":2023,"lang":"en","type":"article","venue":"","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; University of Alberta","funders":"","keywords":"Overfitting; Smoothing; Ground truth; Computer science; Regularization (linguistics); Artificial intelligence; Machine learning; Artificial neural network; Noise (video); Binary classification; Generalization; Pattern recognition (psychology); Mathematics; Support vector machine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01104836,0.001280325,0.001595024,0.001401249,0.001242332,0.00186913,0.002488517,0.002404758,0.00130829],"category_scores_gemma":[0.03417117,0.000735874,0.0009744805,0.001209778,0.00186507,0.00504232,0.002848392,0.003542117,0.0004629949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002369174,"about_ca_system_score_gemma":0.00150102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004497709,"about_ca_topic_score_gemma":0.005536813,"domain_scores_codex":[0.9963737,0.001564802,0.000178804,0.000878949,0.0007578691,0.0002458374],"domain_scores_gemma":[0.9756396,0.01677021,0.001924407,0.00288579,0.002304041,0.0004759444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00056006,0.0002614048,0.007192586,0.0001726059,0.0001839565,0.0001359461,0.0004868471,0.7922004,0.005270181,0.03055145,0.001828942,0.1611556],"study_design_scores_gemma":[0.00001059678,0.00003606265,0.000219618,0.0000115573,0.00001173988,0.00001325622,0.00001798109,0.9813445,0.0008434011,0.01715086,0.000334044,0.000006475124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.119533,0.001100133,0.8749494,0.0008773051,0.00007114263,0.0000821822,0.00008501887,0.001114211,0.002187653],"genre_scores_gemma":[0.8037904,0.0004840315,0.1921078,0.0004437709,0.0001252924,0.000146144,0.0003395032,0.0002136648,0.002349315],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01104836,"threshold_uncertainty_score":0.05843002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2684558467913105,"score_gpt":0.3194399407230387,"score_spread":0.0509840939317282,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}